Viva Biotech is increasing its investment in AI-driven drug discovery as pharmaceutical research moves toward workflows that combine computational design with laboratory validation. The Hong Kong-listed biotech services group reported RMB1.01 billion in first-half 2026 revenue, up 21% year over year, while expanding its AI-enabled discovery platform and advancing commercial CDMO projects.
Viva Biotech Pushes AI Drug Discovery as Biotech Services Business Regains Momentum
The pharmaceutical industry’s use of artificial intelligence is moving beyond experimental molecule-screening projects and toward integrated discovery workflows that connect algorithms with laboratory experiments. Viva Biotech Holdings Group is positioning itself around that shift, expanding its artificial intelligence and computational drug-discovery capabilities while its contract development and manufacturing organization (CDMO) business becomes a larger contributor to revenue.
The company reported RMB1.0065 billion in revenue for the first half of 2026, representing approximately 21% year-over-year growth. Gross profit reached RMB341.7 million, producing a 34% gross margin.
The headline growth, however, masks a more mixed financial picture. Net profit fell to RMB100.9 million, while adjusted non-IFRS net profit was RMB128.3 million. Viva Biotech attributed the decline primarily to lower investment income, unfavorable foreign-exchange movements and higher research and development spending associated with new businesses.
The company’s operating strategy is increasingly centered on two related trends: demand for outsourced pharmaceutical development and the use of AI to make early-stage drug research more efficient.
AI Is Changing the Economics of Drug Discovery
Viva’s CRO business generated RMB405.1 million during the first half, with adjusted gross profit of RMB173.7 million.
The company says its cumulative CRO customer base has reached 1,984, including the world’s 10 largest pharmaceutical companies. Those top 10 customers represented 26.6% of CRO revenue.
A more revealing indicator is the company’s growth in drug targets and computational projects.
By June 30, Viva had delivered more than 108,163 protein structures to customers and studied more than 2,557 independent drug targets. The company delivered 9,278 protein structures and 212 new targets during the first half alone.
That activity illustrates an important change in drug discovery. AI models can rapidly propose molecules, predict protein interactions and explore chemical space, but those predictions still need experimental validation. The result is growing demand for what the industry increasingly describes as a “dry-wet” closed loop: computational models generate or prioritize candidates, laboratories test them, and experimental results feed back into subsequent model development.
Viva says its AI-enabled projects accounted for approximately 14% of CRO revenue in the first half.
Its CADD/AIDD operation—covering computer-aided drug design and AI-driven drug discovery—has participated in 228 projects and served 92 customers.
MARS Targets Multimodal Drug Design
The company is also expanding its technology stack.
Viva has introduced the MARS multimodal integrated algorithm platform, with Pep2MARS focused on peptide, cyclic peptide and macrocycle design.
The platform incorporates three underlying modules called V-Scepter, V-Orb and V-Mantle, which Viva says are being upgraded to improve drug-design efficiency and support practical applications.
This direction is significant because pharmaceutical AI is becoming increasingly multimodal. Rather than relying on a single model to predict a molecule’s properties, modern discovery platforms increasingly combine information about proteins, molecular structures, biological activity and experimental results.
That puts companies such as Viva in competition—and potential partnership—with a broad ecosystem ranging from specialized AI drug-discovery companies to technology platforms developed by Google DeepMind, NVIDIA, Microsoft and Amazon Web Services.
The differentiator is increasingly less about having an AI model alone. It is about whether a company can connect computational predictions to high-quality experimental data.
CDMO Growth Provides a Second Engine
While AI is an important part of Viva’s long-term strategy, the strongest near-term revenue driver was its CDMO business.
Langhua Pharmaceutical generated RMB601.4 million in first-half revenue, up approximately 47% year over year. Adjusted gross profit reached RMB175.1 million, an increase of 12.9%.
Two commercialization projects were responsible for much of that growth.
One peptide program entered commercial stockpiling, while a small-molecule program reached the process performance qualification (PPQ) production stage. These milestones are important because they indicate movement beyond early development toward commercial manufacturing.
Langhua currently has 860 cubic meters of production capacity and is constructing another 400 cubic meters. Viva says its existing capacity can support commercial production volumes expected over the next two years.
The company’s portfolio is also broadening beyond conventional small molecules. New modalities—including peptides, antibodies, XDCs and PROTACs/molecular glues—accounted for 17.7% of CRO revenue.
AI Meets Drug Development Infrastructure
Viva’s strategy reflects a broader industry transition.
AI drug discovery companies initially attracted attention for computational models capable of predicting protein structures or generating candidate molecules. The harder commercial challenge is translating those predictions into validated drug programs.
That requires laboratories, structural biology, medicinal chemistry, pharmacology, process development and eventually manufacturing.
Viva’s combination of CRO, AI-enabled discovery and CDMO capabilities gives it a position across several stages of that chain.
The company’s incubation business extends the strategy further. Viva has invested in or incubated 93 startups, representing nearly 232 pipelines, with 188 in preclinical development and 44 in clinical development. It generated approximately RMB218.4 million from exits during the reporting period and says some proceeds will be reinvested into new projects.
For enterprise pharmaceutical teams, the model points toward an increasingly integrated approach to outsourcing. Rather than treating AI discovery, experimental research and manufacturing as separate activities, drug developers can increasingly seek partners capable of connecting those stages.
The challenge will be proving that computational efficiency translates into better candidates and, ultimately, successful clinical programs.
Viva’s expanding AI platform, growing CRO customer base and rising CDMO contribution suggest the company sees that integration as its long-term competitive advantage.
Market Landscape
AI is reshaping pharmaceutical R&D across target identification, protein structure prediction, molecular generation, virtual screening, peptide design and experimental optimization.
The market is becoming increasingly competitive:
- Google DeepMind has advanced protein-structure and biomolecular prediction through AlphaFold and related research.
- NVIDIA is building AI computing infrastructure and software ecosystems for computational drug discovery.
- Specialized companies such as Recursion, Schrödinger and Insilico Medicine are combining machine learning with biological and chemical datasets.
- CROs and CDMOs are increasingly adding computational capabilities to traditional laboratory and manufacturing services.
- Large pharmaceutical companies are building internal AI capabilities while partnering with external technology providers.
Viva’s approach is notable because it attempts to connect AI-enabled discovery with CRO experimentation and CDMO manufacturing, creating a broader development pipeline rather than a standalone AI product.
Top Insights
- Viva Biotech reported 21% first-half revenue growth as CDMO commercialization projects offset weaker investment income and higher AI-related research spending.
- AI-enabled projects generated 14% of CRO revenue, highlighting increasing demand for computational drug discovery linked directly to laboratory validation.
- Viva’s MARS platform targets peptide, cyclic peptide and macrocycle design, expanding AI capabilities beyond conventional small-molecule discovery.
- Langhua Pharmaceutical’s 47% revenue growth demonstrates the growing contribution of commercial manufacturing to Viva’s integrated CRO-CDMO strategy.
- The company’s dry-wet AI model reflects a wider industry shift toward connecting computational predictions with experimental evidence throughout drug development.
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